Red blood cells (RBC) stored in approved additive solutions undergo a set of metabolic and physicochemical changes referred to as 'storage lesions' reducing the efficacy and safety of older transfused RBC units. Though the consequences of the storage lesion are slowly becoming well documented, a major reason for delayed progress in developing new technologies for quality and safety of RBC transfusion is the lack of global understanding of metabolic decline during storage. There has been interest to utilize high-throughput metabolite profiling for global understanding of RBC metabolic decline but data analysis of complex datasets has been a daunting challenge. The proposed program will develop the first, robust computational platform involving statistical analysis, systems biology of metabolic networks, and data-driven kinetic models to fully interpret and analyze RBC metabolite-profiles in a complete network context. The program will utilize time- course global, quantitative metabolite profiling to track intracellular and extracellular RBC metabolites under standard storage conditions. Deep metabolic understanding will be obtained through computational analysis to quantitatively predict optimal additive solution strategies based on expected biological efficacy, physico- chemical considerations, compound cost, and potential regulatory hurdles. Predicted additives will be chosen for experimental testing in Phase II.